Never Forget Each Time You Could Simply Get A New thiram For Free, And You Never Did?

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?pert bao:BAO_0000361_?analysis. ?assay bao:BAO _0000209 ?measureGroup. ?measureGroup bao:BAO_0000208 ?endpoint. ?endpoint bao:BAO_0000337 ?percentResponse. ?percentResponse bao:BAO_0000195 ?percentResponseValue. } FILTER (?percentResponseValue >= 50) } GROUP BY ?pert HAVING (count(distinct ?assay) >= 3) In this query, we used the inferred relation ""is perturbagen of"", which points to either an endpoint or a bioassay. The query separately checked for bioassay instances and endpoint thiram instances. The syntax allowed for the expression of the notion of ""at least"" in a simple way. Specifically, we used the syntactic extensions available in the ARQ SPARQL [31] implementation. The ""GROUP BY"" extended clause grouped the unique ""?pert"" result MK-8776 ic50 set (?pert is a variable here) in a row-by-row basis. The ""HAVING"" clause applied the lter ""count(distinct ?assay))"" to the result set after grouping. The results of the query were as follows. First, we queried for the compound and obtained: (1) (?pert=) We then used this result (bao:individual_BAO_0000021_646704) for the next query: SELECT ?assay ?percentResponseValue WHERE bao:individual_BAO_0000021_646704 bao:BAO_0000361 ?assay. ?assay bao:BAO_0000209 ?mg. ?mg bao:BAO_0000208 ?endpoint. bao:individual_BAO_0000021_646704 bao:BAO_0000361 ?endpoint. ?endpoint bao:BAO_0000195 ?percentResponseValue. ???UNION bao:individual_BAO_0000021_646704bao:BAO_0000361 ?assay ?assay bao:BAO_0000209 ?mg. bao:individual_BAO_0000021_646704 bao:BAO_0000361 ?endpoint. ?endpoint bao:BAO_0000337 ?percentResponse. ?percentResponse bao:BAO_000195 ?percentResponseValue. FILTER (?rv >= 50) } Here are the final results: (1) (?assay=) (?percentResponseValue=""116.84""xsd:float) ^^ GW4064 ic50 (2) (?assay=) (?percentResponseValue=""106.48""xsd:float) ^^ (3) (?assay=) (?percentResponseValue=""99.42""xsd:float) ^^ Example 3 is a simple illustration to identify compounds with a specific profile (here, active in three assays). The query actually retrieved inferred information, facilitated by the inverse relationship ""is perturbagen of"". Further specification of this query, e.g. by BAO meta target or design sub-classes, would allow to quickly identify individuals based on more complex concepts, for example compounds that are promiscuously active in assays of a specific design and which are therefore likely artifacts. The three query examples illustrate some of the features that can be used in complex search queries with an underlying DL-based ontology. Other features such as role hierarchies, quantifiers, nominals etc. were also used in our ontology. Conclusions We have developed an ontology to describe biological assay and screening results. The BioAssay Ontology (BAO) provides a foundation for standardizing assay descriptions and endpoints and serves as a knowledge model by describing screening experiments and results semantically using description logic (OWL language).

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